ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1 Speed Conrol of Separately Excited dc Motor using Fuzzy Technique Ram kumar karsh 1 , Dr. GK.Choudhary 2 , Chitranjan Kumar 3 'Dept. of Electrical Engineering, NIT Patna, India 1 tnramkarsh@gmail.com 2 H.O.D, Dept. of Electrical Engineering, NIT Patnajndia (girishkrchoudhary, chitranjan_kumar24) @yahoo.co.in Abstract- This paper presents the speed control of a separately excited DC motor using Fuzzy Logic Control (FLC). The Fuzzy Logic Controller designed in this study applies the required control voltage based on motor speed error (e) and its change (ce). The performance of the driver system was evaluated through digital simulations using Simulink. The simulation results show that the control with FLC outperforms PI control in terms of overshoot, steady state error and rise time. Keywords: DC motor, chopper, FLC. I. Introduction DC motors are used in many applications like electric trains, vehicles, cranes and robotics manipulators. They require controlling of speed to perform their tasks. Initially speed control of DC motor has been done by voltage control [1]. Semiconductors too like MOSFET, IGBT and GTO have been used as switching devices to control speed[2]. Due to nonlinearity properties, control of system is difficult and mathematically tedious. To overcome this difficulty, FLC (Fuzzy Logic control) has been developed. FLC is applicable to time variant and nonlinear. Metro system in the sendia of japan is the best application [3]. In this study, the speed response of a separately excited DC motor exposed to fixed armature voltage is studied for both loaded and unloaded operating conditions. Performance of separately excited DC motor is compared for both methods FLC and PI controller for both loaded and unloaded conditions. In this study, chopper circuit has been used as a motor driver. II. Motor model The resistance of the field winding and its inductance are represented by R f and L respectively. The armature, resistance and inductance are represented by R and L respectively. Armature reactions effects are ignored in the description of the separately excited DC motor. This negligence is justifiable to minimize the effects of armature reaction since the motor (SE) used has either interpoles or compensating winding. The fixed voltage V is applied to the field and the field current settles down to a constant value. A linear model of a simple separately excited DC motor consists of a mechanical equation and electrical equation as determined in the following equations: ©2011 ACEEE DOI:01.DEPE02.03.533 J, d(a n dt ^„(pIa-bco m -M load L dt V -I.R -K h 0co (1) (2) The dynamic model of the system is formed using these differential equations and Matlab Simulink blocks as shown in Fig. I, 31 %:•. <:> I — y^ o- _Si- :j iijiii ©- i ■ ;.-; ■.» mjmti C" i: j : «■♦ +0 Fig 1: Simulink Motor mode] Table I. Motor Parameters Parameter Description Value Ra Armature Resistance^) 0.5 L= Armature Inductance (H) 0.003 J* Inertia of rvlotor{kg.m- ■*' £-) 0.0167 K Motor c onstantfNm' Amp) O.S B D amping ratio of mechanical systemptas) 0.0167 —ACEEE ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1 III. Fuzzy Logic Controller (FLC) Description And Design Fuzzy logic control is based on logical relationships like "suitable, not very suitable, high, little high, much and far too much that are frequently used words in people's life. Fuzzy Sets Theory has been introduced to express and process fuzzy knowledge [4], [5] which are used to show linguistic variables. The relation between fuzzy logic and fuzzy set theory that is similar that of relation between Boolean logic and set theory. Fig. 2 shows a basic FLC structure. Fully™*! "♦PUfflHlHJlg fasfaaw ■;';.= - PW-prMaflf Fig. 2 Process Blocks for a Fuzzy Controller FLC is processed for linguistic definitions, while other contrllers work on the accuracy and parameters of system model. While designing FLC, there is no need of knowledge of system model, as a controller. However, less knowledge of control process may result unsatisfactory [6]. A. Defining inputs, outputs: As the bigger speed error the bigger controller input is expected. So, FLC is designed to minimize speed error. Due to that FLC uses error (e) and change of error (ce) for linguistic variables which are generated from the control rules. Control variable (cu) is applied to achieve angular value (alpha), which determines duty cycle. e(k)=[co(k)-co(k)]*K IE ce(k)=[e(k)-e(k-l)]*K ca(k)=[a(k)-a(k-l)]*K 2CE 3 co. (3) Here K 1E K, CE and K 3 ca are each gain coefficients and K is a time index. wr[k] 9 -&>■% hH (UilYWrtlr* %MflW N Fig. 3. Block diagram of the DC motor control At nominal value of motor speed the error (e) gives its smallest value, and at maximum value of motor speed the error gives its larger value, with range -200 and 200. ©2011 ACEEE DOI:01.DEPE.02.03.533 1 eHH^**rtr|^ ^ A/Y^^* - ' — -QJ2-L-- ~rg ^J-¥--"- + ,[. -4J — ™ t- i : - t j™ a = \ I ...., .j 1 ;.,,.._ _j.„, j..^ n n 1 M z : ■i 1 w :iziz:jz:iz::n ... 1 ...: -1 7 1 I- ■ -1 \ r — ; - ; [ t H 1 h - 1 F+- Fig. 4. Change of Error W- Ai A^ As A. A 5 A« A? M As Am e + - - - - - + + - ce - - — — - - — + - - Fig. 5. Dynamic Signal Analysis B. Defining membership functions and rules: System speed comes to reference value by means of the defined rules. For example, first rule on Table determines, 'if (e) is PL and (ce) is PL than (c.) is NL According to this rule, if error value is positive large and change of error value is positive large than output, change of alpha will be negative large. In this condition, corresponding A4 interval in Fig 5, motor speed is smaller than reference speed and still wants to decrease strongly. This is one of the worst conditions in control process. Because of the fact that alpha is smaller than the required value, its value can be increased by giving output PL value. This state corresponds to motor voltage decreasing. All conditions in control process are shown in Fig.5. Membership functions have been used to convert inputs and outputs from crisp value to linguistic term. Linguistic terms are represented here by seven membership functions shown in table. . Fig. 6. Linguistic rules for angle (alpha) determination for driver circuit. It will for a) speed error, b) Change in speed error, c) 32 Change of alpha ACEEE ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1 Table II. The Rule Data Base ee m NM XS Z PS PM PL m. PL PL PL PL mi 2 2 XM PL PL PL P.I/ PS 2 2 m PL PM PS PS PS 2 2 z PL PM Pi 2 NS KM KL p& 2 2 KM KS KS KM M PM 2 2 NS KM KL M M PI 2 2 MM M M M M IV. Driver circuit and modeling DC chopper has been used to drive the motor also changes average value of load voltage applied from a fixed DC source by switching a power switch. I. y. Vo Load Vo i ton, ■* — *■■*■ i©r ■-\ I Fig 7. Operating principle and output waveform of Driver Using Fig 7, the average output voltage can be calculated as V t, do t +t« l on I off -V (4) Where V is the DC source voltage, v, can be controlled o do using three methods: *Hold t fixed and change t (frequency modulation) *Hold period (t + t ff ) fixed and change t ff /t rate (pulse width modulation) *change t fl and t separately. (Combination of first and second method) One-quadrant DC chopper and general waveforms for continuous current conditions are shown in fig. 8 (Xjwi: nflcli Fig 8. simple power circuit of a one quadrant DC chopper 10 « imax irm iD imin Vo DT fr I 0T T Fig 9. General waveform for current continuous condition Fig. 10. DC Chopper model Fundamentally, the operating principle of driver model is based on the comparison of two signals [7] . One of the sig- nals is a triangular waveform which represents one PWM is used to control average output voltage period of 2 KHz chop- ping frequency and other one is fixed linear signal which represents time equivalent of alpha triggering (t ). Since chopping frequency is 2 KHz, the amplitude of triangular waveform starts from zero and reaches 1 / 2. 10 3 = 0.0005 value. On the other hand, the alpha signal from controller is multi- plied by 0.0005/360 value to calculate the time corresponding to this angle. Alpha signal and triangular signal are Uj and U2 variables of ' IF' block used in simulation model shown in Fig. 10, respectively. •.+ OuOOOS v ... vdo- IN 1 tfs} — ' Fig 11. input and output signal of driver model ©2011 ACEEE DOL01.DEPE.02.03.533 33 ^ACEEE ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1 V. Control simulation In FLC model gainl, gain2 and gain3 define change of error, error and change of alpha scaling factors respectively. Simu lation results are shown for 50 nm load applied at 0.6s. Simu- lation result for PI controller for loaded and no loaded condi- tion is shown in table iii IZZ1 S:tf*2 Aitf f I I ) ■ M ■ -.- ■ ' .... . ■:-. H» r :n Slit*' Fig 12. Fuzzy Logic Controller Simulink model Fig 13. a) Speed response of PI controller for used motor Fig 13. b) Speed response of Fuzzy controller for used motor Table III performance analysis of system CI: Kp=100, Ki=15 Tr: Rise time C2: Kp=200,Ki=25 e : Steady state error C3: Kp=300, Ki=28 %M Percentage Maximum overshoot C4: Kp=500, Ki=30 CI, C2 Different Kp and Ki coefficients (a) For different loads p I Land Jtf.Y soy 5GX CI SO£ 5.955 y.Cii 5 52 ■?„ -0.5Q5 -0.2SS -0.131 C2 'A.% 5.655 5. till 5 5.631 s„ -6,25 -D.15S -0.15 C3 %14 5. '31 5 5:7315 5:7315 s„ -0.1S -0.15 -0.12 C4 %u. i.S5S 5.5S5 5.S95 ■?„ -6.1175 -0.05 -0.075 FLC 5fl4 0.S1 2.64 4.53 •Br, Q.QOdl -0.0045 -0.01S (b) Unloaded operation Criteria PI FLC CI C2 CS C4 Tr 0.141 0.141 '■ !-i! 0.141 0.036 #r, -0.533 -0.5 '5 -0.415 -0.262 -0.01 &w f 5.527 5.631 5:711 5.S94 0.6S Percent overshoot (%M ) and steady state error (e ) are measured for different load. Conclusion Fuzzy Logic Controllers are a suitable option to make speed regulation in DC motors and AC motors. The quality of the control obtained with FLC's at the first tries is commonly good because is based on the knowledge of an expert. It can not be said the same about conventional controllers. The single human based reasoning used on a FLC can be very useful to overcome nonlinearities of any kind of plants in a logical way. The experience gained from these works has allowed us to attack another systems of very different nature obtaining satisfactory results. Comparison between PI controller responses and FLC responses is shown in table iii and shows that FLC gives better performance than PI controller in terms of overshoot , steady state error and rise time. Also show that FLC is more sensitive to load changes. It would be necessary to use a more complex intelligent control system, i.e. Adaptive Fuzzy System, Neuro-Fuzzy System, in order to obtain a better performance on speed control Refrences [I] Chan, C. C, Low Cost Electronic Controlled Variable Speed Reluctance Motors, IEEE Transactions on Industrial Electronics, Vol. IE-34, No. I. 95-100. February 1987. [2] Khoei, A.. Hadidi, Kh., Microprocessor Based Closed-Loop Speed Control System for DC Motor Using Power Mosfet. Electronics Circuits and Systems IEEE international Conference ICECS'96, Vol. 2, 1247-1250, 1996. [3] C. Elmas, "Fuzzy Logic Controllers", Seqkin Publishing, April- 2003 [4] L. A. Zadeh, " Fuzzy Sets" Informal Control, -01. 8p, p 338- 353, 1965. ©2011 ACEEE DOr.Ol.LTEPE.02.03.533 34 vc ACEEE ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 201 1 [5] L. A. Zadeh, '. Outline of a new approach to the analysis complex systems and decision processes" IEEE Trans. Syst. Man Cybem, vol. SMC-3, pp. 2844, 1973 [6] Y. Tipsuwan, Y. Chow, "Fuzzy Logic Microcontroller Implementation for DC Motor Speed Control". IEEE. 1999. [7] F. Rahman, .'Lectures 18 Control of E€-DC Conveners", Power Electronics. ELEC424019240. Ram kumar Karsh was born in 1986 in a small village of lanjgir-champa district of Chattisgarh. He received B.E. (Electron- ics & Tele communication) in 2009 with first class from Govt Engineering College, Bilaspur, India. He is pursuing M.Tech. degree with specialization Control Sys- tems from Department of Electrical Engi neering, National Institute of Technology (NITP), Patna, India. His field of interest includes fuzzy logic, control systems. Prof. Girish Kumar Choudhary was born in August, 1959 in a small village. He did his matriculation in 1975, and was enrolled for diploma in Electrical Engineering in the same year. He completed his diploma in Electrical Engineering securing first position in the state of Bihar in 1979. He got his B.Sc (Engineering) degree in Electrical in 1985 securing first class first from Patna University, Patna with distinction. He acquired his Ph.D degree in 1998 from Patna University. He joined BCE Patna (Presently NIT Patna) in Ian. 1988 as Lecturer in Electrical Engineering and promoted as Associate Professor Electrical Engineering in 1996. Subsequently he became Professor in 2006. Presently he is working as Professor & Head of Electrical Engineering at NIT Patna. He is also holding the post of Chairman, HMC, NIT Patna. He has many publications in National and International lournals and Conferences. He has also achieved the distinction of getting his research product "Adapters for Laptops and others Electronic Devices." Patented (No. 235642 dated 10.07.2009). Prior to joining NIT Patna he has also worked in All India Radio (AIR) Patna and Videsh Sanchar Nigam Limited, Arvi, Pune. Chitranjan kumar received B.E. (Elec- tronics & Tele communication) in 2009 with first class from D.K.TE.S Ichalkaranji ,Kolhapur, India. He completed M.Tech. degree with specialization Control Systems in 2011 with first class from Department of Electrical Engineering from National In stitute of Technology (NITP), Patna, India. His field of interest includes fuzzy logic, signal processing, control systems ©2011 ACEEE DOr.Ol.DEPE.02.03.533 35 ^ACEEE